Predicting Postoperative Delirium in Older Patients: a multicenter retrospective cohort study
Bibliographic record
Abstract
Abstract Background The number of elective surgeries for older individuals is on the rise globally. Machine learning may improve risk assessment with an impact on surgical planning and postoperative care. Preoperative cognitive assessment may facilitate early identification of postoperative delirium (POD). This study aims to estimate the predictive ability of machine learning models for POD using pre-and/or perioperative features, with a specific focus on adding neuropsychological assessments prior to surgery. Materials and Methods This retrospective cohort study analyzed data from the multicenter PAWEL study and its PAWEL-R substudy, encompassing older patients (≥70 years) undergoing elective surgeries across five medical centers from July 2017 to April 2019. A total of 1624 patients were included, with POD diagnosis made before discharge. Data included demographics, clinical, surgical, and neuropsychological features collected pre- and perioperatively. Machine learning model performance was evaluated using the area under the receiver operating characteristic curve (AUC), with permutation testing for significance and SHapley Additive exPlanations (SHAP) to identify effective neuropsychological assessments. Results In this cohort of 1624 patients, 52.3% (N=850) were male, with a mean [SD] age of 77.9 [4.9] years. Predicting POD before surgery using demographic, clinical, surgical, and neuropsychological features achieved an AUC of 0.79. Incorporating all pre- and perioperative features into the model yielded a slightly higher AUC of 0.82, with no significant difference observed ( P = .19). Notably, cognitive factors alone were not strong predictors (AUC=0.61). However, specific tests within neuropsychological assessments, such as the Montreal Cognitive Assessment memory subdomain and Trail Making Test Part B, were found to be crucial for prediction according to SHAP analysis. Conclusion and Relevance Preoperative risk prediction for POD can increase risk awareness in presurgical assessment and improve postoperative management in patients with a high risk for delirium. Highlights Analyzed 1624 older patients (≥70 years) undergoing elective surgeries across five medical centers from July 2017 to April 2019. Established machine learning model to predict postoperative delirium before surgery. Preoperative cognition enhances predictive performance, comparable to models incorporating all pre- and perioperative features. Montreal Cognitive Assessment memory subdomain and Trail Making Test Part B drive the cognition-based prediction. Perioperative surgical features, such as the duration of the surgery, are important predictors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".